KU Leuven Department of Development and Regeneration, Woman and Child, 3000 Leuven, Belgium.
Department of Obstetrics and Gynecology, Leuven University Fertility Center, University Hospital Leuven, 3000 Leuven, Belgium.
Biomed Res Int. 2019 Jul 25;2019:3673060. doi: 10.1155/2019/3673060. eCollection 2019.
There is a great need for a noninvasive diagnosis for endometriosis. Several biomarkers and biomarker panels have been proposed. Biomarker models consisting of CA-125, VEGF, Annexin V, and glycodelin/sICAM-1 were previously developed by our group. The objective of our current study was to assess the impact of technical and biological variability on the performance of those previously developed prediction models in a technical verification and a validation setting. The technical verification cohort consisted of peripheral blood plasma samples from a subset of the patients included in the original study of Vodolazkaia (99 women with and 37 women without endometriosis). The validation study was done in plasma samples of an independent patient cohort (170 women with and 86 women without endometriosis). Single immunoassays were used for CA-125, VEGF-A, sICAM-1, Annexin V, and glycodelin. Statistical analyses were done using univariate and multivariate (logistic regression) approaches. The previously reported prediction models for endometriosis had a low performance in both the technical verification and validation setting. New prediction models were developed, which included CA-125, Annexin V, and sICAM-1, but CA-125 was the only marker that was retained in the models across the technical verification and validation study. Overall, successful validation of a biomarker model depends on several factors such as patient selection, collection methods, assay selection/handling, stability of the marker, and statistical analysis and interpretation. There is a need for standardized studies in large, well-defined patient cohorts with robust assay methodologies.
内异症的无创诊断需求巨大。已经提出了几种生物标志物和生物标志物组合。我们小组之前开发了由 CA-125、VEGF、膜联蛋白 V 和糖蛋白 110/sICAM-1 组成的生物标志物模型。本研究的目的是评估技术和生物学变异性对以前开发的预测模型在技术验证和验证设置中的性能的影响。技术验证队列由 Vodolazkaia 原始研究中包含的患者亚组的外周血血浆样本组成（99 名内异症患者和 37 名非内异症患者）。验证研究是在独立患者队列的血浆样本中进行的（170 名内异症患者和 86 名非内异症患者）。CA-125、VEGF-A、sICAM-1、膜联蛋白 V 和糖蛋白 110 分别用于单免疫测定。使用单变量和多变量（逻辑回归）方法进行统计分析。以前报道的内异症预测模型在技术验证和验证设置中的性能均较低。开发了新的预测模型，其中包括 CA-125、膜联蛋白 V 和 sICAM-1，但在技术验证和验证研究中，CA-125 是唯一保留在模型中的标志物。总体而言，生物标志物模型的成功验证取决于多个因素，例如患者选择、采集方法、检测选择/处理、标志物稳定性以及统计分析和解释。需要使用稳健的检测方法在大型、明确的患者队列中进行标准化研究。
